Technology for building and managing data models

ABSTRACT

Techniques for building and managing data models are provided. According to certain aspects, systems and methods may enable a user to input parameters associated with building one or more data models, including parameters associated with sampling, binning, and other factors. The systems and methods may automatically generate program code that corresponds to the inputted parameters and display the program code for review by the user. The systems and methods may build the data models and generate charts and plots depicting aspects of the data models. Additionally, the systems and methods may combine data models and select champion data models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/977,618 (filed May 11, 2018 and entitled“TECHNOLOGY FOR BUILDING AND MANGING DATA MODELS”), which claims benefitof the filing dates of U.S. Provisional Patent Application No.62/589,444 (filed Nov. 21, 2017 and entitled “TECHNOLOGY FOR BUILDINGAND MANAGING DATA MODELS”); U.S. Provisional Patent Application No.62/592,975 (filed Nov. 30, 2017 and entitled “TECHNOLOGY FOR BUILDINGAND MANAGING DATA MODELS”); U.S. Provisional Patent Application No.62/615,286 (filed Jan. 9, 2018 and entitled “TECHNOLOGY FOR BUILDING ANDMANAGING DATA MODELS”); U.S. Provisional Patent Application No.62/621,784 (filed Jan. 25, 2018 and entitled “TECHNOLOGY FOR BUILDINGAND MANAGING DATA MODELS”); U.S. Provisional Patent Application No.62/632,679 (filed Feb. 20, 2018 and entitled “TECHNOLOGY FOR BUILDINGAND MANAGING DATA MODELS”); and U.S. Provisional Patent Application No.62/633,859 (filed Feb. 22, 2018 and entitled “TECHNOLOGY FOR BUILDINGAND MANAGING DATA MODELS”)— which are hereby incorporated by referencein their entireties.

TECHNICAL FIELD

This disclosure relates generally to building and managing data modelsand, in particular, to enabling users to effectively specify variousparameters associated with data modeling and presenting results of thedata modeling within a specified and managed approach.

BACKGROUND

Data modeling is the process of creating one or more data models usingstatistical and data science techniques. Predictive modeling is a typeof data modeling that uses those techniques to forecast outcomes, wherea data model may be made up of a set of predictors that influence thedata model output. Predictive modeling serves many applications,including uplift modeling, archaeology, customer relationshipmanagement, health care, algorithmic trading, and insurance. Further,predictive modeling incorporates various techniques, including naïveBayes classifiers, k-nearest neighbor algorithms, majority classifiers,support vector machines, random forests, boosted trees, classificationand regression trees (CARD), multivariate adaptive regression splines(MARS), neural networks, ordinary least squares, generalized linearmodels (GLM), logistic regression, generalized additive models (GAM),ensemble learning methods (ELM), robust regression, and semi-parametricregression, among others.

GLMs, in particular, model the linear relationship between a predictedresponse(s), or dependent variable(s), and a set of predictors, or a setof independent variables. In property and casualty insurance ratemakingapplications, the predicted variable may be one of the following: claimfrequency, claim severity, pure premium, or loss ratio. However, thereare technological challenges associated with building and combining GLMs(and other types of models). Accordingly, there is an opportunity fortechniques and platforms for effectively and efficiently building andcombining various types of models.

BRIEF SUMMARY

The present embodiments may relate to, inter alia, data modeling,including building and combining data models using various platforms andtechniques. The embodiments described herein relate particularly tovarious aspects of enabling users to input assumptions and parameters inassociation with building data models, and automatically facilitatingthe building of data models according to those inputs. The embodimentsdescribed herein additionally relate to techniques for combining datamodels and techniques for selecting a “champion” data model.

In one aspect, a computer-implemented method within a computing deviceof enabling the management of data models may be provided. The methodmay include: generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where data, inputs and model outputs are stored, a datato be partitioned, a set of variables to be binned, a set ofidentifications for (1) at least one of a training dataset and avalidation dataset, and (2) modeling data, and a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, (iii) modeling method(s), (iv) model ensemble process(es)and/or (v) a challenger model comparison. The method may further includegenerating, by the processor, the modeling output according to the modelbuild partition, and displaying, in the user interface, a set of resultsassociated with generating the modeling output, the set of resultsincluding: (a) a set of model level results, and/or (b) a set ofvariable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

For instance, enabling the user to input the set of variables to bebinned may include enabling the user to input, for each of the set ofvariables, (i) a binning technique, (ii) a number of bins, and/or (iii)a binned value. Additionally or alternatively, enabling the user toinput a data to be partitioned may include enabling the user to input,via the user interface, (i) a stratification selection, (ii) a samplepercent, and/or (iii) a random seed.

In some embodiments, enabling the user to input the set ofidentifications for the modeling data may include enabling the user toinput (i) a model type, (ii) a distribution, (iii) a link function,and/or (iv) a unique identifier. Additionally or alternatively, enablingthe user to input the set of identifications for the modeling data mayinclude enabling the user to input a set of predictors and a responsevariable.

In some embodiments, enabling the user to input the set of selectionsassociated with the exploratory data analysis (EDA) may include enablingthe user to input whether to run the EDA using the entire dataset orusing the training dataset. Additionally or alternatively, enabling theuser to input the set of selections associated with the variableselection may include enabling the user to input (i) whether to run thevariable selection using the entire dataset or using the trainingdataset, (ii) a set of model effects, and/or (iii) a set of variableselection techniques.

In some embodiments, enabling the user to input the set of selectionsassociated with the modeling methods may include enabling the user toinput (i) whether to generate the modeling output using the entiredataset or using the training dataset, (ii) a model iterationidentification, and/or (iii) a set of model effects.

In some embodiments, displaying the set of model level results mayinclude displaying, in the user interface, a set of predictionstatistics. Additionally or alternatively, displaying the set ofvariable level results may include displaying, in the user interface, aset of main effects and a set of interaction relativity plots.

In some embodiments, the modeling output may include multiple modeloutputs, and the method may further include combining the multipleoutputs using either an additive technique or a multiplicativestatistical technique. Additionally or alternatively, the method mayfurther include, after combining the multiple models: selecting achampion model.

In another aspect, a computer system for enabling the management of datamodels may be provided. The system may include: a user interface, amemory storing a set of computer-executable instructions, and aprocessor with the user interface and memory. The processor may beconfigured to execute the computer-executable instructions to cause theprocessor to: generate a model build partition, including enabling auser to input, via the user interface: a storage location where data,inputs and model outputs are stored, a data to be partitioned, a set ofvariables to be binned, a set of identifications for (1) at least one ofa training dataset and a validation dataset, and (2) modeling data, anda set of selections associated with (i) an exploratory data analysis(EDA), (ii) a variable selection, (iii) modeling method(s), (iv) modelensemble process(es) and/or (v) a challenger model comparison. Theprocessor may be further configured to generate the modeling outputaccording to the model build partition, and cause the user interface todisplay a set of results associated with generating the modeling output,the set of results including: (a) a set model level results, and/or (b)a set of variable level results. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

For instance, the set of variables to be binned may include, for each ofthe set of variables, (i) a binning technique, (ii) a number of bins,and/or (iii) a binned value. Additionally or alternatively, the set ofidentifications for the modeling data may include a set of predictorsand a response variable.

In some embodiments, the set of selections associated with theexploratory data analysis (EDA) may include whether to run the EDA usingthe entire dataset or using the training dataset. Additionally oralternatively, the set of selections associated with the variableselection may include (i) whether to run the variable selection usingthe entire dataset or using the training dataset, (ii) a set of modeleffects, and/or (iii) a set of variable selection techniques. The set ofselections associated with the modeling methods may also include (i)whether to generate the modeling output using the entire dataset orusing the training dataset, (ii) a model iteration identification,and/or (iii) a set of model effects.

In some embodiments, the set of model level results may include a set ofprediction statistics. Additionally or alternatively, the set ofvariable level results may include a set of main effects and a set ofinteraction relativity plots.

In some embodiments, the modeling output may include a first modeloutput and a second model output, and the processor may be furtherconfigured to combine the first model output and the second model outputusing either an additive technique or a multiplicative technique.Additionally or alternatively, the processor may be further configuredto, after combining the first model output and the second model output,select a champion model.

In another aspect, a computer-implemented method in a computing deviceof enabling the management of data models may be provided. The methodmay include (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or a Generalized Linear Model (GLM) to be createdand/or programmed by the computer processor; and/or a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, and/or (iii) a challenger model comparison; (2) generating,by the computer processor, the modeling output according to the modelbuild partition; and/or (3) displaying, in the user interface, a set ofresults associated with generating the modeling output, the set ofresults including: (a) a set of model level results, and/or (b) a set ofvariable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The system may include: a user interface; amemory storing a set of computer-executable instructions; and/or aprocessor interfaced with the user interface and the memory, andconfigured to execute the computer-executable instructions to cause theprocessor to: (1) generate a model build partition, including enabling auser to input, via the user interface: a storage location where amodeling output is to be stored; a set of variables to be binned; a setof identifications and/or user selections for modeling data, and/or aGeneralized Linear Model (GLM) to be created and/or programmed by thecomputer processor, and/or a set of selections associated with (i) anexploratory data analysis (EDA), (ii) a variable selection, and/or (iii)a challenger model comparison; (2) generate the modeling outputaccording to the model build partition, and/or (3) cause the userinterface to display a set of results associated with generating themodeling output, the set of results including: (a) a set model levelresults, and/or (b) a set of variable level results. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method in a computing deviceof enabling the management of data models may be provided. The methodmay include (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or a Generalized Additive Model (GAM) to becreated and/or programmed by the computer processor; and/or a set ofselections associated with (i) an exploratory data analysis (EDA), (ii)a variable selection, and/or (iii) a challenger model comparison; (2)generating, by the computer processor, the modeling output according tothe model build partition; and/or (3) displaying, in the user interface,a set of results associated with generating the modeling output, the setof results including: (a) a set of model level results, and/or (b) a setof variable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The method may include: a user interface; amemory storing a set of computer-executable instructions; and aprocessor interfaced with the user interface and the memory, andconfigured to execute the computer-executable instructions to cause theprocessor to: (1) generate a model build partition, including enabling auser to input, via the user interface: a storage location where amodeling output is to be stored; a set of variables to be binned; a setof identifications and/or user selections for modeling data, and/or aGeneralized Additive Model (GAM) to be created and/or programmed by thecomputer processor; and/or a set of selections associated with (i) anexploratory data analysis (EDA), (ii) a variable selection, and/or (iii)a challenger model comparison; (2) generate the modeling outputaccording to the model build partition, and/or (3) cause the userinterface to display a set of results associated with generating themodeling output, the set of results including: (a) a set model levelresults, and/or (b) a set of variable level results. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method in a computing deviceof enabling the management of data models may be provided. The methodmay include (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or an Ensemble Learning Method (ELM) to becreated and/or programmed by the computer processor; and/or a set ofselections associated with (i) an exploratory data analysis (EDA), (ii)a variable selection, and/or (iii) a challenger model comparison; (2)generating, by the computer processor, the modeling output according tothe model build partition; and/or (3) displaying, in the user interface,a set of results associated with generating the modeling output, the setof results including: (a) a set of model level results, and/or (b) a setof variable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The computer system may include: a userinterface; a memory storing a set of computer-executable instructions;and/or a processor interfaced with the user interface and the memory,and configured to execute the computer-executable instructions to causethe processor to: (1) generate a model build partition, includingenabling a user to input, via the user interface: a storage locationwhere a modeling output is to be stored; a set of variables to bebinned; a set of identifications and/or user selections for modelingdata, and/or an Ensemble Learning Method (ELM) to be created and/orprogrammed by the computer processor; and/or a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, and/or (iii) a challenger model comparison; (2) generate themodeling output according to the model build partition, and/or (3) causethe user interface to display a set of results associated withgenerating the modeling output, the set of results including: (a) a setmodel level results, and/or (b) a set of variable level results. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method of building aGeneralized Linear Model (GLM), Generalized Additive Model (GAM), orEnsemble Learning Method (ELM) model and then model ratemakinginformation may be provided. The method may include, via one or moreprocessors: (1) accepting user input that identifies a file from whichto retrieve ratemaking input data; (2) accepting user input thatidentifies a file to which store results generated from a user-definedGLM, GAM, or ELM, respectively, created using user-selections actingupon the ratemaking input data; (3) accepting user-selected variablesrelated to the user-defined GLM, GAM, or ELM, respectively, to becreated; (4) translating the user-selected variables into a programminglanguage code that can be compiled, executed, and/or run by one or moreprocessors to create the user-defined GLM, GAM, or ELM, respectively,that is defined by the user-selected variables; (5) executing theprogramming language code to create the user-defined GLM, GAM, or ELM,respectively; (6) feeding the ratemaking input data into theuser-defined GLM, GAM, or ELM, respectively, created to model theratemaking input data and generate modeling results; and/or (7)displaying the modeling results to facilitate modeling ratemakinginformation. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

In another aspect, a computer system configured to build a GeneralizedLinear Model (GLM), Generalized Additive Model (GAM), or EnsembleLearning Method (ELM) model and then model ratemaking information may beprovided. The computer system may include one or more processors, and/ora graphical user interface, configured to: (1) accept user input thatidentifies a file from which to retrieve ratemaking input data; (2)accept user input that identifies a file to which store resultsgenerated from a user-defined GLM, GAM, or ELM, respectively, createdusing user-selections acting upon the ratemaking input data; (3) acceptuser-selected variables related to the user-defined GLM, GAM, or ELM,respectively, to be created; (4) translate the user-selected variablesinto a programming language code that can be compiled, executed, and/orrun by the one or more processors to create the user-defined GLM, GAM,or ELM, respectively, that is defined by the user-selected variables;(5) execute the programming language code to create the user-definedGLM, GAM, or ELM, respectively; (6) feed the ratemaking input data intothe user-defined GLM, GAM, or ELM, respectively, created to model theratemaking input data and generate modeling results; and/or (7) displaythe modeling results to facilitate modeling ratemaking information. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

Systems or computer-readable media storing instructions for implementingall or part of the methods described above may also be provided in someaspects. Systems for implementing such methods may include one or moreof the following: a special-purpose assessment computing device, amobile computing device, a remote server, one or more sensors, one ormore communication modules configured to communicate wirelessly viaradio links, radio frequency links, and/or wireless communicationchannels, and/or one or more program memories coupled to one or moreprocessors of the mobile computing device, or remote server. Suchprogram memories may store instructions to cause the one or moreprocessors to implement part or all of the method described above.Additional or alternative features described herein below may beincluded in some aspects.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred aspects which havebeen shown and described by way of illustration. As will be realized,the present aspects may be capable of other and different aspects, andtheir details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the presentdisclosure. It should be understood that each figure depicts anembodiment of a particular aspect of the present disclosure. Further,wherever possible, the following description refers to the referencenumerals included in the following figures, in which features depictedin multiple figures are designated with consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 is an illustration of an exemplary technique for building asingle data model in accordance with one aspect of the presentdisclosure;

FIG. 2 illustrates an exemplary interface associated with a structurecomponent associated with model building in accordance with one aspectof the present disclosure;

FIG. 3 illustrates an exemplary interface associated with a binningsub-component associated with model building in accordance with oneaspect of the present disclosure;

FIG. 4 illustrates an exemplary interface associated with a samplingsub-component associated with model building in accordance with oneaspect of the present disclosure;

FIG. 5 illustrates an exemplary interface associated with a datasub-component associated with model building in accordance with oneaspect of the present disclosure;

FIG. 6 illustrates an exemplary interface associated with an EDAsub-component associated with model building in accordance with oneaspect of the present disclosure;

FIG. 7 illustrates an exemplary interface associated with a variableselection sub-component associated with model building in accordancewith one aspect of the present disclosure;

FIG. 8 illustrates an exemplary interface associated with a challengermodel sub-component associated with model building in accordance withone aspect of the present disclosure;

FIG. 9 illustrates an exemplary interface associated with a predictionstatistics sub-component associated with model building in accordancewith one aspect of the present disclosure;

FIG. 10 illustrates an exemplary interface associated with a maineffects sub-component associated with model building in accordance withone aspect of the present disclosure;

FIG. 11 illustrates an exemplary interface associated with aninteraction relativity plots sub-component associated with modelbuilding in accordance with one aspect of the present disclosure;

FIG. 12A is an illustration of an exemplary multiplicative technique forcombining two data models in accordance with one aspect of the presentdisclosure;

FIG. 12B is an illustration of an exemplary additive technique forcombining two data models in accordance with one aspect of the presentdisclosure;

FIG. 13A illustrates an exemplary interface associated with amultiplicative data model combination technique in accordance with oneaspect of the present disclosure;

FIG. 13B illustrates an exemplary interface associated with an additivedata model combination technique in accordance with one aspect of thepresent disclosure;

FIG. 13C illustrates an exemplary interface associated with selecting achampion data model in accordance with one aspect of the presentdisclosure;

FIG. 14 depicts a block diagram of an exemplary computer-implementedmethod of enabling management of data models in accordance with oneaspect of the present disclosure;

FIG. 15 illustrates a hardware diagram of an exemplary computing devicein which the functionalities as discussed herein may be implemented, inaccordance with one aspect of the present disclosure;

FIG. 16 illustrates an exemplary computer-implemented process flow;

FIG. 17 illustrates an exemplary modeling folder structure for an autoinsurance-related embodiment;

FIG. 18 illustrates exemplary files and folders associated with aserver; and

FIG. 19 illustrates exemplary user inputs associated with creatinglibraries.

The Figures depict aspects of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternate aspects of the structures andmethods illustrated herein may be employed without departing from theprinciples of the invention described herein.

DETAILED DESCRIPTION

In general, data modeling is used in various contexts to assess risk ininsurance, finance, and other industries and professions. For example,in life insurance assessments, data models may incorporate the analysisof mortality data, the production of life tables, and the application ofcompound interest. As another example, health insurance modeling mayfocus on the analysis of rates of disability, morbidity, mortality,fertility, and other factors.

The systems and methods of the present disclosure offer a variety ofdata science and statistical data modeling methods. Generally, a datamodel may assess the linear or non-linear relationship between aresponse variable (i.e., a dependent variable), and a set of predictors(i.e., input or independent variables). For example, in property andcasualty insurance ratemaking applications, the response variable may beone of: claim frequency, claim severity, pure premium, or loss ratio.Additionally, examples of ratemaking predictors may be: type of vehicle,age, or marital status for auto insurance; and construction type,building age, or amount of insurance (AOI) for homeowners insurance.

According to some embodiments, techniques and platforms for building,managing, and combining data models are discussed. The techniques andplatforms may build data models using a model build partition and amodel assessment partition. The model build partition may incorporate aset of data transformation and modeler techniques, and the modelassessment partition may incorporate a model level comparison and avariable level comparison.

According to some embodiments, the systems and methods may be supportedby a server computer, and data may be uploaded to and stored on theserver. Additionally, the server may support client login, for exampleusing a username and password, or other techniques.

The systems and methods offer numerous benefits. In particular, thesystems and methods effectively and efficiently enable users to inputdata modeling parameters, and automatically populate applications withthe requisite programming code that reflects the inputted parameters.The systems and methods further employ data model combining andselecting techniques that effectively and efficiently identify accurateand useful data models for subsequent use. It should be appreciated thatadditional benefits are envisioned and realized.

Exemplary Model Building Techniques

FIG. 1 is an illustration of a technique 100 for building a single datamodel, according to certain aspects. As illustrated in FIG. 1 , thetechnique 100 may include a model build partition 101 and a modelassessment partition 102. The model build partition 101 may include thefollowing components and subcomponents: a structure component 103, adata transformation component 104 (with the following sub-components:binning 111 and sampling 112), and a modeler component 107 (with thefollowing sub-components: data 113, exploratory data analysis (EDA) 114,set ref 115, variable selection 116, benchmark model 117, and challengermodel comparison 118).

In one embodiment, setting a reference level 115 after EDA 114 in singletool may be a new and innovative approach. Also believed to be unique isbuilding a benchmark model 117 and computing prediction statistics 119before building challenger models 118 within a single tool.

The model assessment partition 102 may include the following componentsand subcomponents: a model level comparison 108 (with the followingsub-components: prediction statistics 119 and other plots 120), and avariable level comparison 109 (with the following sub-components: maineffects relativity plots 121 and interaction relativity plots 122).

Exemplary Structure Component Interface

The structure component 103 may aim to automatically create a modelingfolder structure using programming language. According to someembodiments, a user such as an analyst may identify a location of thedata. FIG. 2 illustrates an exemplary interface 200 associated with thestructure component 103. The interface 200 may include an input window201 into which an individual may input a directory where modeling datais to be saved. Additionally, the interface 200 may include a rootdirectory input window 202 into which an individual may input a rootdirectory where the modeling output is to be saved.

The interface 200 may further include a coding window 205 that may bedisplayed alongside the other windows 201, 202, or in a differentlocation. In association with the individual inputting information intoone or more of the windows 201, 202, the coding window 205 mayautomatically populate with program code that corresponds to theinputted information. For example, the coding window 205 automaticallyupdates lines of program code for the inputted modeling data directory(“DataRaw”). In this regard, the platform enables automatic program codegeneration corresponding to the inputted information.

The binning sub-component 111, as discussed with respect to FIG. 1 , mayaim to combine categories of insufficient data so that the modelprediction may be stable/credible. According to some embodiments, thesystems and methods may support the following functionalities:displaying a list of variables in a dataset to enable analysts or otherindividuals to correctly identify variables, transforming options forbinned values within the platform, and enabling name assignment fornewly-created binned variables (or enabling the platform to assigndefault names).

Exemplary Data Transformation Component

In general, the present embodiments may include a data transformationcomponent. The objectives and functionality of the data transformationcomponent may include preparing the data for modeling—which may includebinning and random sampling.

The objective and functionality of binning may be to combine categoriesof insufficient data. The new processes within the tool (later referredto as the SMART Tool herein) may include displaying a list of variablesin the data set to help analysts correctly identify variables;transforming options for binned values within the tool; and/or allowingname assignment for the newly created binned variable or allowing theSMART Tool to assign default names.

The objective and functionality of random sampling may be to divide thedata into training and validation data. This division facilitatescreating of build and validate datasets. The integration andorganization of the data transformations within a single tool isbelieved to be a new approach to modeling processes.

Exemplary Binning Component Interface

FIG. 3 illustrates an example interface 300 associated with the binningsub-component 111 of the data transformation component. The interface300 may include an input window 301 into which an individual may addvariables to be binned. Additionally, the interface 300 may include abinning input window 302 into which an individual may specify thefollowing information: the variable, the binning method (e.g.,pseudo-quantile binning or bucket binning), a number of bins (e.g., ten(10)), a new variable name, and/or other information). The interface 300may further include an output window 303 into which an individual mayinput a desired name for the output dataset.

The interface 300 may further include a coding window 305 that may bedisplayed alongside the other windows 301, 302, 303, or in a differentlocation. In association with the individual inputting information intoone or more of the windows 301, 302, 303, the coding window 305 mayautomatically populate with program code that corresponds to theinputted information. For example, the coding window 305 automaticallyadds lines of program code for the inputted variables “R_MODEL_YEAR” and“R_TENURE”. For further example, the coding window 305 automaticallyupdates a line of program code to name the output data set as“PDIL_Bin”. In this regard, the platform enables automatic program codegeneration corresponding to inputted information associated withvariable binning.

Exemplary Sampling Component Interface

FIG. 4 illustrates an example interface 400 associated with the samplingsub-component 112 of the data transformation component. The interface400 may include an input window 401 into which an individual may specifya dataset to be sampled (as shown: “DATA_OUT.PDIL_BIN”). Additionally,the interface 400 may include a stratification window 402 into which anindividual may input a parameter(s) by which to stratify. The interface400 may further include an options window 403 into which an individualmay specify the following parameters: a sample percent (or a number ofrows), a random seed number, and/or other information. The interface 400may further include an output window 404 into which an individual mayinput a desired name for the output dataset.

The interface 400 may further include a coding window 405 that may bedisplayed alongside the other windows 401-404, or in a differentlocation. In association with the individual inputting information intoone or more of the windows 401-404, the coding window 405 mayautomatically populate with program code that corresponds to theinputted information. For example, the coding window 405 automaticallyupdates lines of program code for the inputted dataset to be sampled(“DATA_OUT.PDIL_BIN”). In this regard, the platform enables automaticprogram code generation corresponding to inputted information associatedwith data sampling.

According to some embodiments, the data sub-component 113 of the modelercomponent 107 facilitates or performs various functionalities. Inparticular, the data sub-component 113 enables for the identification ofmodeling data, dynamically selects the target variable and distributionparameters, selects unique identifiers, and identifies the types ofexplanatory variables, among other features.

Exemplary Data Component Interface

As shown in FIG. 1 , the modeler component 107 may include a datasub-component 113. The objectives and functionality of the datasub-component 113 may include allowing for the identification ofmodeling data; dynamic selection of the target variable anddistribution; selecting unique identifiers; and/or identifying the typesof explanatory variables. The dynamic selection of target variables andthe associated distributions within a single tool is believed torepresent a new approach to modeling processes.

FIG. 5 illustrates an exemplary interface 500 associated with the datasub-component 113 of the modeler component 107. The interface 500 mayinclude an input window 501 into which an individual may specify variousinformation and parameters, including: a dataset, a model type, a targetrisk variable(s), a target exposure variable, a distribution type (e.g.,Poisson, negative binomial), a link function, a unique identifier, a setof classification variables, and a set of continuous variables.

The interface 500 may further include a coding window 505 that may bedisplayed alongside the window 501, or in a different location. Inassociation with the individual inputting information into the window501, the coding window 505 may automatically populate with program codethat corresponds to the inputted information. For example, the codingwindow 505 automatically updates a line of program code to reflect theselected Poisson distribution. In this regard, the platform enablesautomatic program code generation corresponding to inputted informationassociated with the data sub-component.

According to some embodiments, the exploratory data analysis (EDA)sub-component 114 of the modeler component 107 facilitates or performsvarious functionalities. In particular, the EDA sub-component 114calculates statistics of the observed actual target variable,automatically stores and manages the univariate analysis results to thecorresponding modeling folder, facilitates data error identification,and provides a general overview of the data before a multivariateanalysis, among other features.

Exemplary Eda Component Interface

As shown in FIG. 1 , the modeler component 107 may include anexploratory data analysis sub-component 114. The objectives andfunctionality of the EDA sub-component 114 may include calculatingstatistics of the observed actual target variable; automatically storingand managing the univariate analysis results to the correspondingmodeling folder; facilitating data error identification; and/orproviding a general overview of the data before multivariate analysis.The automatic storage and management of univariate analysis within asingle tool is believed to represent a new approach to modelingprocesses.

FIG. 6 illustrates an exemplary interface 600 associated with the EDAsub-component 114 of the modeler component 107. The interface 600 mayinclude an input window 601 into which an individual may specify variousinformation and parameters, including a dataset analysis selection. Theinput window 601 may further including information associated with theEDA sub-component, including univariate analysis, interaction detection,and output. The interface 600 may further include a data display window605 that may be displayed alongside the window 601, or in a differentlocation. According to some embodiments, the data display window 605 maydisplay various data associated with the EDA sub-component 114.

According to some embodiments, the variable selection sub-component 116of the modeler component 107 facilitates or performs variousfunctionalities. In particular, the variable selection sub-component 116may facilitate the incorporation of multiple variable selectiontechniques into a single process, output selection results in asummarized table format, and automatically store and manage the variableselection results within a tools data structure, among other features.

Exemplary Variable Selection Component Interface

As shown in FIG. 1 , the modeler component 107 may include a variableselection sub-component 116. The objectives and functionality of thevariable selection sub-component 116 may include facilitating theincorporation of run multiple variable selection techniques into asingle process; outputting the selection results in a summarized tableformat; and/or automatically storing and managing the variable selectionresults within the tools data structure. The automatic storage andmanagement of variable selection analysis within a single tool isbelieved to represent a new approach to modeling processes.

FIG. 7 illustrates an exemplary interface 700 associated with thevariable selection sub-component 116 of the modeler component 107. Theinterface 700 may include an input window 701 into which an individualmay specify various information and parameters, including: a datasetselection, a set of model effects, and a set of methods. The interface700 may further include a data display window 705 that may be displayedalongside the window 701, or in a different location. According to someembodiments, the data display window 705 may display various dataassociated with the variable selection sub-component 116, including asummary table of the analysis.

According to some embodiments, the challenger model sub-component 118 ofthe modeler component 107 facilitates or performs variousfunctionalities. In particular, the challenger model sub-component 118may facilitate the creation of generalized linear models, otherstatistical and data science models, output and organize parameterestimates in an easily-interpretable manner, compute predictionstatistics and summarize general information about the model,automatically store and manage modeling results within the toolsstructure and create the appropriate output files, among other features.

Exemplary Challenger Model Component Interface

As shown in FIG. 1 , the modeler component 107 may include a challengermodel sub-component 118. The objectives and functionality of thechallenger model sub-component 118 may include facilitating the creationof generalized linear models; other statistical and data science models;outputting and organizing parameter estimates in an easily interpretablemanner; computing prediction statistics and summarizing generalinformation about the model; and/or automatically storing and managingmodeling results within the tools structure and creating the appropriateoutput files. The automatic storage and management of model results,parameters, and output within a single tool is believed to represent anew approach to modeling processes.

FIG. 8 illustrates an exemplary interface 800 associated with thechallenger model sub-component 118 of the modeler component 107. Theinterface 800 may include an input window 801 into which an individualmay specify various information and parameters, including: a dataselection, model iteration information, and a set of model effects.

The interface 800 may further include a coding window 805 that may bedisplayed alongside the window 801, or in a different location. Inassociation with the individual inputting information into the window801, the coding window 805 may automatically populate with program codethat corresponds to the inputted information. In this regard, theplatform enables automatic program code generation corresponding toinputted information associated with the challenger model sub-component118.

Exemplary Model & Variable Level Comparison

As shown in FIG. 1 , the present embodiments may include a model levelcomparison component 108 and a variable level comparison component 109.These components may provide a number of prediction statistics whichcompare the prediction accuracy between model iterations. Discussedfurther below, FIGS. 9 to 11 illustrate prediction statistics associatedwith the model level comparison, and the main and interaction effects ofthe variable level comparison, respectively.

Exemplary Prediction Statistics Component Interface

FIG. 9 illustrates an exemplary interface 900 associated with theprediction statistics sub-component 119 of the model-level comparison108. The interface 900 may include a model iteration selection window901, a model summary 902, and a chart 903. The model iteration selectionwindow 901 enables an individual to select one or more model iterationsfor mapping or charting. The model summary 902 indicates variousversions as well as data associated therewith (e.g., AIC, BIC, Lift,etc.). The chart 903 displays relevant data for the selected modeliterations (as shown: 1_Val and 6_Val).

Exemplary Main Effects Component Interface

FIG. 10 illustrates an exemplary interface 1000 associated with the maineffects sub-component 121 of the variable-level comparison 109. Theinterface 1000 may include an effect selection window 1001, a leveltable 1002, and a relativity plot 1003. The effect selection window 1001may enable an individual to select one or more model effects (as shown:marital status). The level table 1002 may display data associated withthe selected effect (as shown: a percentage breakdown of married versussingle people). The relativity plot 1003 may display a relativity plotassociated with the selected effect. The relativity line display 1004allows specification of multiple model iterations to be displayed alongwith confidence intervals.

Exemplary Relativity Plots Component Interface

FIG. 11 illustrates an exemplary interface 1100 associated with theinteraction relativity plots sub-component 112 of the variable-levelcomparison 109. The interface 1100 may include a selection window 1101,a level table 1102, and a relativity plot 1103. The selection window1101 may enable an individual to select an interaction and an iteration.The level table 1102 may indicate various data associated with certainlevels. The relativity plot 1103 may display a relativity plotassociated with the selections.

Exemplary Multiplicative Technique

FIG. 12A is an illustration of an exemplary multiplicative technique1200 for combining two models. In particular, the technique 1200illustrates a first model 1201 and a second model 1202, each of whichmay be generated or built according to the technique 100 as discussedwith respect to FIG. 1 . The technique 1200 includes a multiplicativecombiner 1203 which may take, as inputs, the first model 1201 and thesecond model 1202, and may output a combined model 1204. FIG. 12Adepicts an optional refitting of the model 1204 after combination.

According to some embodiments, the multiplicative combiner 1203 maymultiply the values included in the first model 1201 and the secondmodel 1202 to generate the combined model 1204. Generally, themultiplicative process addresses theoretical issues associated withcombining distributions in a multiplicative way including theappropriate reconstruction of combined parameter estimates. In certainembodiments, the theoretical mathematical constructs of the combineddistribution are automatically created. Additionally, the technique 1200includes a champion selector 1205 that may facilitate the selection,storage, and management of a champion model.

Exemplary Additive Technique

FIG. 12B is an illustration of an exemplary additive technique 1210 forcombining two models. In particular, the technique 1210 illustrates afirst model 1211 and a second model 1212, each of which may be generatedor built according to the technique 100 as discussed with respect toFIG. 1 . The technique 1210 includes an additive combiner 1213 which maytake, as inputs, the first model 1211 and the second model 1212, and mayoutput a combined model 1214. FIG. 12B depicts an optional refitting ofthe model 1214 after combination.

According to some embodiments, the additive combiner 1213 may add thevalues included in the first model 1211 and the second model 1212 togenerate the combined model 1214. Generally, the additive processaddresses theoretical issues associated with combining distributions inan additive way including the appropriate reconstruction of combinedparameter estimates. In certain embodiments, the theoreticalmathematical constructs of the combined distribution are automaticallycreated. Additionally, the technique 1210 includes a champion selector1215 that may facilitate the selection, storage, and management of achampion model.

Exemplary Multiplicative Model Combination Interface

In general, the present embodiments may include a multiplicative processmultiplicative combiner, such as shown in FIG. 12A. The objectives andfunctionality of this combiner may include allowing analysts to combinemultiple single models into a combined model. The multiplicative processaddresses theoretical issues with combining distributions in amultiplicative way including the appropriate reconstruction of combinedparameter estimates. Theoretical mathematical constructs of the combineddistribution may be created automatically. The combiner may alsofacilitate selection, storage, and management of a champion model.

More specifically, FIG. 13A illustrates an exemplary interface 1300associated with the multiplicative model combination technique. Theinterface 1300 may include an input window 1301 into which an individualmay specify various information and parameters, including: a frequencymodel dataset, a severity model dataset, a unique identifier, anexposure variable, model iteration numbers for each of the frequency andseverity models, and an output dataset name.

The interface 1300 may further include a coding window 1305 that may bedisplayed alongside the window 1301, or in a different location. Inassociation with the individual inputting information into the window1301, the coding window 1305 may automatically populate with programcode that corresponds to the inputted information. For example, when theindividual enters the unique identifier in the window 1301, the codingwindow 1305 may automatically generate program code indicating theentered unique identifier (as shown: “O_POLICY_NUMBER”). In this regard,the platform enables automatic program code generation corresponding toinputted information associated with the multiplicative modelcombination technique.

Exemplary Additive Model Combination Interface

In general, the present embodiments may include an additive processadditive combiner, such as shown in FIG. 12B. The objectives andfunctionality of this combiner may include allowing analysts to combinemultiple single models into a combined model. The additive processaddresses theoretical issues with combining distributions in an additiveway including the appropriate reconstruction of combined parameterestimates. Theoretical mathematical constructs of the combineddistribution may be created automatically. The combiner may alsofacilitate selection, storage, and management of a champion model.

More specifically, FIG. 13B illustrates an exemplary interface 1310associated with the additive model combination technique. The interface1310 may include an input window 1311 into which an individual mayspecify various information and parameters, including: a modelingdataset, a unique identifier, an exposure variable, a set of componentmodels, and an output dataset name.

The interface 1310 may further include a coding window 1315 that may bedisplayed alongside the window 1311, or in a different location. Inassociation with the individual inputting information into the window1311, the coding window 1315 may automatically populate with programcode that corresponds to the inputted information. For example, when theindividual enters the unique identifier in the window 1311, the codingwindow 1315 may automatically generate program code indicating theentered unique identifier (as shown: “O_POLICY_NUMBER”). In this regard,the platform enables automatic program code generation corresponding toinputted information associated with the additive model combinationtechnique.

Exemplary Champion Model Selection Interface

The present embodiments may facilitate selection of a champion model.Champion model results may be stored and managed within the applicationstructure. Work files, temporary models and output may be automaticallymanaged and cleaned up as needed. The integration of folder structureand automated processes for evaluation, cleanup and management of themodel results within a single tool is believed to represent a newapproach to modeling processes.

FIG. 13C illustrates an exemplary interface 1320 associated withselecting a champion model. The interface 1320 may include an inputwindow 1321 into which an individual may specify various information andparameters, including an iteration number for a champion model.

The interface 1320 may further include a coding window 1305 that may bedisplayed alongside the window 1321, or in a different location. Inassociation with the individual inputting information into the window1321, the coding window 1325 may automatically populate with programcode that corresponds to the inputted information. For example, when theindividual enters the iteration number in the window 1321, the codingwindow 1325 may automatically generate program code indicating theentered iteration number (as shown: “1”). In this regard, the platformenables automatic program code generation corresponding to inputtedinformation associated with the champion model selection.

Exemplary Method of Enabling Management of Data Models

FIG. 14 depicts a block diagram of an exemplary computer-implementedmethod 1400 of enabling management of data models. In particular, themethod 1400 may be associated with building one or more models,combining the one or more models, and selecting a champion model.According to some embodiments, the method 1400 may be performed by acomputing device, such as a server computer, configured with orconfigured to connect to a user interface, where a user or individualmay interact with the user interface. It should be appreciated that thefunctionalities of the method 1400 are exemplary, and that additional oralternative functionalities are envisioned.

The method 1400 may begin when the computing device generates a modelbuild partition. In particular, the computing device may enable (block1405) the user to input, via the user interface, a storage locationwhere one or more of the following may be stored: data, a set of inputs,and a set of model outputs. In some embodiments, the storage locationmay be local to the computing device or to another device (e.g., withina distributed database).

The computing device may further enable (block 1410) the user to input,via the user interface, data to be partitioned and/or a set of variablesto be binned. In particular, the computing device may enable the user toinput, for each of the set of variables, (i) a binning technique, (ii) anumber of bins, and/or (iii) a binned value. The computing device mayenable (block 1415) the user to input, via the user interface, a set ofidentifications for (i) at least one of a training dataset and avalidation dataset, and/or (ii) modeling data. According to certainembodiments, the modeling data may be associated with one or moredifferent modeling techniques, including GLM, GAM, ELM, and/or others.In one implementation, the set of identifications for the modeling datamay include a model type, a distribution, a link function, and/or aunique identifier. In an additional or alternative implementation, theset of identifications for the modeling data may include a target riskvariable and a target exposure variable.

The computing device may enable (block 1420) the user to input, via theuser interface, a set of selections associated with (i) an EDA, (ii) avariable selection, (iii) a set of model methods, (iv) a set of modelensemble process, and/or (v) a challenger model comparison. In someembodiments, the set of selections associated with the EDA may includean input of whether to run the EDA using the validation dataset and thetraining dataset (i.e., the entire dataset), or using the trainingdataset. Further, the set of selections associated with the variableselection may include (i) whether to run the variable selection usingthe entire dataset or using the validation dataset, (ii) a set of modeleffects, and/or (iii) a set of variable selection techniques.Alternatively or additionally, the set of selections associated with themodeling methods may include (i) whether to generate the modeling outputusing the entire dataset or using the training dataset, (ii) a modeliteration identification, and/or (iii) a set of model effects. Thecomputing device may optionally enable (block 1425) the user to input,via the user interface, (i) stratification selection, (ii) a samplepercent, and/or (iii) a random seed.

The computing device may generate (block 1430) the modeling outputaccording to the model build partition. In some embodiments, themodeling output may be stored in the storage location inputted in block1405, for access by the computing device and/or other devices.

The computing device may display (block 1435), in the user interface, aset of results associated with the generating the modeling output, wherethe set of results may include (i) a set of model level results, and/or(ii) a set of variable level results. In some embodiments, the set ofmodel level results may include a set of prediction statistics, and theset of variable level results may include a set of main effects and/or aset of interaction relativity plots.

In certain embodiments, the modeling output may include a first modeloutput and a second model output (and optionally additional modeloutputs). The computing device may combine (block 1440) multiple modeloutputs (i.e., the first model output and the second model output) usingeither an additive technique or a multiplicative technique, to generatea combined model(s). Additionally, the computing device may select(1445) a champion model from the any of the initial model outputs andthe combined model(s). In some embodiments, the computing device mayselect the champion model according to various factors and parameters.

Exemplary Computing Device

FIG. 15 illustrates a hardware diagram of an exemplary computing device1510 in which the functionalities as discussed herein may beimplemented. In particular, the computing device 1510 may support themodel building, comparing, and selecting functionalities as discussedherein.

The computing system 1510 may include a processor 1559 as well as amemory 1556. The memory 1556 may store an operating system 1557 capableof facilitating the functionalities as discussed herein as well as a setof applications 1551 (i.e., machine readable instructions). For example,one of the set of applications 1551 may be a modeling application 1552configured to facilitate various functionalities discussed herein. Itshould be appreciated that one or more other applications 1553 areenvisioned.

The processor 1559 may interface with the memory 1556 to execute theoperating system 1557 and the set of applications 1551. According tosome embodiments, the memory 1556 may also include modeling data 1558,such as modeling data that may be used by the modeling application 1552.The memory 1556 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers. In one implementation, the computing device 1510 may interfacewith external storage, such as one or more databases. Additionally oralternatively, the memory 1556 (and/or any external storage) may beincluded as part of a distributed database.

The computing system 1510 may further include a communication module1555 configured to communicate data via one or more networks 1520.According to some embodiments, the communication module 1555 may includeone or more transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 1554. For example, the communication module 1555 mayreceive, from an external electronic device, various datasets, modelingparameters, and/or the like.

The computing device 1510 may further include a user interface 1562configured to present information to a user and/or receive inputs fromthe user. As shown in FIG. 15 , the user interface 1562 may include adisplay screen 1563 and I/O components 1564 (e.g., ports, capacitive orresistive touch sensitive input panels, keys, buttons, lights, LEDs).According to some embodiments, a user may access the computing device1510 via the user interface 1562 to review information, make changes,input modeling parameters, and/or perform other functions.

In some embodiments, the computing device 1510 may perform thefunctionalities as discussed herein as part of a “cloud” network or mayotherwise communicate with other hardware or software components withinthe cloud to send, retrieve, or otherwise analyze data.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code may be adapted to beexecuted by the processor 1559 (e.g., working in connection with theoperating system 1557) to facilitate the functions as described herein.In this regard, the program code may be implemented in any desiredlanguage, and may be implemented as machine code, assembly code, bytecode, interpretable source code or the like (e.g., via Golang, Python,Scala, C, C++, Java, Actionscript, Objective-C, Javascript, CSS, XML).In some embodiments, the computer program product may be part of a cloudnetwork of resources.

Smart Tool Overview

The present embodiments may relate to, inter alia, a SMART (StatisticalModeler using Advanced Ratemaking Techniques) Tool, or Application, thatmay include two graphical-user interfaces (GUIs) that allow the user tomodel ratemaking information (e.g., frequency, severity, and/or purepremium). The SMART Tool may contain several custom tasks that help theuser to produce a finished model.

The SMART Tool, in one aspect, is designed to help users who may nothave much SAS or other coding knowledge build a successful pricingmodel. However, being somewhat familiar with both environments (SASStudio and Visual Analytics) and basic ratemaking principles, especiallythose covered in CAS Exam 5 Basic Ratemaking, may be helpful to use theSMART Tool to its full potential.

A. Generalized Linear Models (GLMs)

GLMs model the linear relationship between a response, or dependent,variable, and a set of predictors, also called input or independentvariables. In property and casualty insurance ratemaking applications,the response variable may be usually one of the following: claimfrequency, claim severity, pure premium, or loss ratio.

Examples of ratemaking predictors are type of vehicle, age, or maritalstatus for personal auto insurance; construction type, building age, oramount of insurance (AOI) for homeowners insurance.

The GLM relationship is written as the following:g(μ_(i))=β₀+β₁x_(i1)+β₂x_(i2)+ . . . +β_(p)x_(ip).

Where g(μ_(i)) is the link transformation applied to the mean μ_(i), β₀is the intercept, and β_(i) is the coefficient applied to each x_(i).For ratemaking, a log link is applied, yielding multiplicative factors,which is the most commonly used rating plan in ratemaking, and whereμ_(i)=exp(β₀+β₁x_(i1)+β₂x_(i2)+ . . . +β_(p)x_(ip))=e{circumflex over( )}β₀×e{circumflex over ( )}β₁x_(i1)× . . . ×e{circumflex over( )}β_(p)x_(ip).

B. Generalized Additive Models (GAMs)

GAMs are models in which relationships between the individual predictorsand the dependent variable follow smooth patterns that may be linear ornonlinear. The smooth relationships may be estimated simultaneously topredict the expected value of a response variable Y according to thefollowing: g(E(Y))=β₀+f₁(x₁)+f₂(x₂)+ . . . +f_(m)(x_(m)).

In this equation, the response variable, Y, is related to a set ofpredictor variables, x_(i), where E(Y) denotes the expected value andg(Y) denotes the link function that links the expected value to thepredictor variables x_(i). A set of functions f_(i) denote functionswith a specified parametric form (e.g., polynomial), non-parametricform, or semi-parametric form, that are estimated by non-parametricmeans. Generally, GAMs enable regularization, interpretability,flexibility, and automation. Accordingly, GAMs provide a regularized andinterpretable solution, especially in situations in which the modelcontains nonlinear effects.

C. Ensemble Learning Methods (ELMs)

ELMs employ multiple learning algorithms using a finite set ofalternative models to assess predictive performance. Generally, the goalof ELMs is to combine multiple hypotheses to form a hopefully betterhypothesis. ELMs may be implemented using various algorithms, such asdecision trees (e.g., Random Forest), and may be implementedsequentially or in parallel. Additionally, ELMs may be of various types,including: Bayes optimal classifier, bootstrap aggregating, boosting,Bayesian parameter averaging, Bayesian model combination, bucket ofmodels, and stacking. Because ELMs employ multiple learning algorithms,the results may be more accurate than those of single learningalgorithms, as the results may average out biases, reduce variance, andreduce instances of overfitting.

D. Modeler

The entire process may be executed on a dedicated server. Data may beobtained from various sources, but in order to use the SMART Toolapplication, the data should preferably be uploaded and stored on theserver. The SMART Tool may allow the user to custom build models andperform their statistical analysis on the predictors they select.

Two exemplary process flows described herein are suggestions on how touse the SMART Tool to its full potential. As detailed further herein,FIG. 1 and associated text explains the process to build a single model,includes a model build partition and a model assessment partition. FIGS.12A and 12B and associated text explains the process to build a combinedmodel, with FIG. 12A focusing on the multiplicative process, and FIG.12B focusing on the additive process.

In one embodiment, visualization may take place in SAS Visual Analyticsor other program, which may be on the dedicated server. Datasets createdin the Modeler may be automatically pushed to the server with eachiteration performed.

E. Server Files and Folders

Creating Files and Folders may be considered Task A in one embodiment.The server's Files and Folders, shown in FIG. 18 , may be where thepermanent files are located. This may be where a user will access theSMART Tool or application tasks. The SMART Tool or application may alsocreate a ROOT directory in the first step to hold the datasets and filescreated by each task.

Libraries may be where the datasets are stored. The SMART Tool orapplication may also create several libraries, or folders/files, thathold datasets created from, or during, the modeling process.

A process flow may be used to visualize the order of the tasks. In oneembodiment, the user may use a combination of tasks to create their owncustomized process flow. For instance, to create a custom process flow,the user may be able to click an icon and select a Process Flow tab orbutton from a dropdown list or menu. After which, a user may drag anddrop various tasks to add them to new process flow that may begraphically depicted. Also, the user may be allowed to remove a task,such as by clicking upon an icon and hitting delete.

In order to have accurate models, the data may need to be properlycleaned and prepared beforehand. A “response variable” may be calculatedfrom selected target risk variables and target exposure variables in thetool prior to building a statistical model are calculated. Exemplaryresponse variables may include, for insurance-related embodiments,calculated Frequency, calculated Severity, and/or calculated Loss Ratio.

It should be noted that users should make sure the variable types theyare using are correct. For example, if the user wants to use a numericvariable as a categorical variable in the modeling, then they will needto change its variable type to a categorical variable before using theModeler. Otherwise, the variable will be filtered out, or be modeledincorrectly due to the underlying code.

F. Define Libraries and Create Folders

The first step in building a model (Task A) may be to tell the SMARTTool or application where the data is located, and where to store newdata. This task may ask for two user inputs, as shown in FIG. 19 . Thisfirst user input may be a DATA_IN (SOURCE) Directory. This may be theserver path where the input dataset is located.

The second user input may be a DATA_OUT (ROOT) Directory. This may bethe server path where the output datasets created by the tool arelocated. If the indicated directory does not exist, this task willcreate one. Datasets from two potential Task B's (such as Random Sample,and Variable Binning) may appear here.

In one embodiment, the ROOT Directory may have five (5) subdirectoriesfor insurance-related embodiments: (1) FREQ: holds frequency datasetsand associated files; (2) SEV: holds severity datasets and associatedfiles; (3) PP: holds pure premium datasets or combined risk premiumdatasets and associated files; (4) FINAL: holds the champion modelsselected; and/or (5) TEMP: temporary files are kept here, such as theintermediate dataset from EDA. This task may also create SAS or otherlibraries with corresponding names. This way, datasets located undereach subdirectory may also be located in the library of the same name.

It should be noted that this task may need to be run once for eachcomponent, and each time the user starts a SAS Studio session. Forexample, if the user is building frequency and severity models using thesame dataset, Task A (which may include defining libraries and/orcreating files and folders) only needs to be run only once. If the userlogs out or desires to build a different component, they will need torun Task A.

G. Variable Binning

Variable Binning may be an optional Task B, but is recommended. Binningcreates groups for continuous variables that may facilitate modeling. Asexamples, “pseudo-quantile binning” may return equal number of resultsper bin. Currently the default option, pseudo-quantile is anapproximation of quantile binning, and returns similar results. “Bucketbinning” may create bins of equal width. And, “winsorized binning” maybe similar to bucket binning except that the tails are cut off to ensurea smoother binning result and remove outlier influence.

H. Random Sampling

Like the previous task, Random Sampling is another optional Task B thatis recommended, but not required. The user may choose to use only oneTask B, or both in any order.

The random sampling task may be based upon stratified sampling. The datamay be partitioned into two groups based upon the selected stratifyingvariable(s), then a sample from each group may be taken based upon apredefined portion. In one embodiment, the sampled variables may beindicated in a “Selected” column created in this task by assigning eachvalue either a “0” or “1”, and the GLM (or GAM, ELM, or the like)created or programmed by the system may use the sampled data to do themodeling.

Further, a random seed may be used to specify the initial seed forrandom number generation. This feature may be useful if the user wantedto compare or replicate several sampling attempts later by making surethe initial seed is the same for all attempts.

I. Model Declaration

The Model Declaration task may be used to specify the target riskvariable, target exposure variable, model type (e.g., Frequency,Severity, or Pure Premium for insurance-related embodiments), andpredictors and/or interaction variables used in the Variable Selectiontask.

A Response Variable may be calculated from a target risk variable and/ora target exposure variable. In insurance-related embodiments, in aFrequency Model, the Response Variable may be Claim Count per exposure,or a frequency variable. In a Severity Model, the Response Variable maybe Loss Amount per event, or a severity variable. In a Pure PremiumModel, the Response Variable may be Loss Amount per exposure, or a purepremium variable. A Pure Premium Model may also be used to refit PurePremium models previously created as combination of frequency andseverity models.

A Weight Variable may allow users to give more ‘weight’ to rows thatcarry greater risk. In a Frequency Model, usually Exposure (i.e., EarnedHouse Years) may be used, and the Response and Weight variables maybecome Frequency=Claim Count/Exposure.

In a Severity Model, Claim Count may be used, and Severity=Loss/ClaimCount.

In a Pure Premium Model, Exposure (EHY) may be used, andPP=Loss/Exposure.

In some embodiments, a drop down menu may be used to select thespecified model type. This feature may be used to direct output datasetsto the right folder when they are modeled.

The user may select which distribution best fits the data. For instance,Poisson may be used for Frequency. Negative Binomial may also be usedfor Frequency. Gamma may be used for Severity and/or Pure Premium.Tweedie may be used for Pure Premium, and may use a combination ofPoisson (Frequency) and Gamma (Severity).

A link function may be used that provides flexibility in transformingthe equation and relating the model prediction to its predictors. A LogLink may have the property of producing a multiplicative structure,making it the most intuitive link function to use in ratemaking.

A unique identifier may be used to identify the correct rows to mergetogether in future tasks, and may use a Policy Number or another similarunique variable.

Variable Selection is highly recommended to ensure the model is usingstatistically valid predictors. All potential predictors may be selectedto be used in the model, including those to be used for interactionterms. Continuous Variables may be used that are numeric variable thatrepresent a measurement on a continuous scale, such as age or AOI.

Offset variables may also be used. For instance, a ‘fixed’ variable thatis part of the rating plan, but is not given a coefficient in the GLM,such as a base rate or deductible. The GLM equation is represented asg(μ₁)=β₀+β₁x_(i1)+β₂x_(i2)+ . . . +β_(p)x_(ip)+offset.

Preferably, the offset variable is a continuous variable.

Some variables might have a combined effect on the target variable. Inother words, the effect of one predictor might depend upon the value ofanother, and vice-versa. The combined effect of these variables isreferred to as an interaction term.

A variable selection method may be performed to further narrow down thepredictors. The output is a variable selection summary that the user canuse. As examples, “backward selection” may start with all predictors,and each step may delete the variable with the least contribution untila stopping condition is reached. “Forward selection” may start with nopredictors, and each step may add the most significant variable until astopping condition is reached.

“Stepwise selection” may be used, and may be a modification of forwardselection, but effects selected may be removed later. Stepwise selectionuses the same methods of adding and removing effects as forward andbackward selection, respectively. If at any step an effect is notsignificant, it may be removed.

“Lasso selection” may be used that includes the sum of the absolutevalues of the regression coefficients that may be constrained to besmaller than a specified parameter. “Variance based selection” mayidentify a group of variables that jointly explain the maximum amount ofvariance in the response variables. “Random forest selection” may beused that generates a random forest that evaluates variable importanceusing decision trees.

In some embodiments, the two previously created frequency and severitymodels may be combined into one Pure Premium model. Additionally oralternatively, Pure Premium models from all components may be combined.For example, the user may create an All Peril Pure Premium Model fromseparate fire, water, weather, hurricane, etc. Peril Pure PremiumModels.

Exemplary Process Flow

FIG. 16 illustrates an exemplary computer-implemented process flow 1600.The process flow 1600 may include determining input data location 1602,and/or allow the user to select files or folders with the input data tobe input into the model(s) created by the SMART Tool. For instance, ininsurance-related embodiments, the input data may include ratemakinginput data; customer data; data related to historical or currentinsurance claims; data related to premiums, discounts, loss, exposures;and other types of input data, including that discussed elsewhereherein.

The process flow 1600 may include setting up file, folder, and/orlibrary structures 1604, such as the structures discussed elsewhereherein. The structures may include structures for input and output(i.e., results generated by the model(s)) structures. In one embodiment,the user may enter input and output folders or files, and the system mayautomatically build or set up the input and/or output folder and filestructures.

The process flow 1600 may include data transformation 1606. Forinstance, variable binning and random sampling may be used to transformthe input data. The data may be grouped into appropriate sizes thatincrease the accuracy of the modeling. Initial data groups may also becombined to increase modeling accuracy. Other transformations may beperformed on the input data, including those transformations discussedelsewhere herein.

As discussed elsewhere herein, the process flow 1600 may includeexploratory data analysis; variable selection; and model creation 1608(e.g., GAM, GLM, ELM, or other data science models). The variableselection may determine which variables should be selected. Forinstance, miles; home, vehicle or customer age; home or vehiclefeatures; smart home features; autonomous or semi-autonomous vehiclefeatures; state features; geographic related features; home or vehicletelematics data features; and/or other data features, including thosediscussed elsewhere herein, may be selected for variousinsurance-related or rating models, including those related to auto orhomeowners insurance.

Turning briefly to FIGS. 3 to 5 , for instance, on the left hand side ofthe user interface, the user may be presented with a series of optionsor selections. The right hand side depicts the programming code, such asobject code, that would have to be typed in by the user if they were notusing the user interface. The user interface and process describedherein alleviates the need for the user to be a programmer and/or tounderstand how to write the programming code, such as SAS. For instance,the user selections of options on the left hand side of the userinterface automatically populate the programming code on the right handsider of the user interface.

The present embodiments allow users to build code by selecting optionsor icons—with the resulting code having no errors or being free fromprogramming errors. In other words, the user interface acts as atemplate to efficiently or effectively build code, such as SAS code,without any programming knowledge and that is error free. As a result,the user can focus on the options or selections that they would like intheir model, and not on the actual coding itself.

Machine learning and/or artificial intelligence techniques may also beused to increase the accuracy of the models, including GLM, GAM, and/orELM models, created. For instance, as new claim data becomes available,machine learning techniques may be used to further revise the models toaccount for the new information. This may be especially useful as newertechnologies become available to customers—such as new make and modelsof vehicles, including electric or hybrid vehicles, or new autonomous orsemi-autonomous features for auto insurance; or new smart or intelligenthome features, or new types of construction or construction materialsfor homeowners or renters insurance.

The process flow 1600 may include displaying the modeling results 1610,such as on a display screen. After the GLM or other model (e.g., GAM orELM) is created, and the input data is analyzed or processed by the GLMor other model (e.g., GAM or ELM), a processor may translate the modeloutput. Analysis may then be performed to determine how “good” the modelis. For instance, confidence levels may be estimated. Also, for autoinsurance models, frequency and severity models, and/or other models(e.g., property damage or bodily injury models) may be combined intoensembles in order to increase accuracy.

Certain embodiments may relate to, inter alia, insurance, banking, orfinancial services. As an example, FIG. 17 illustrates an exemplarymodeling folder structure for an auto insurance-related embodiment. TheSMART Tool may automatically create a modeling folder structure usingthe programming language created by the SMART Tool. Analysts mayidentify the location of data within the tool using the structure. Asshown in FIG. 17 , the file or folder structure may include a high levelfolder associated with Coverage, and sub-folders may include Frequency,Severity, Pure Premium, Final, Temp, and/or other sub-folders.

Additional Exemplary Embodiments

In one aspect, a computer-implemented method in a computing device ofenabling the management of data models and/or creating models may beprovided. The method may include (1) generating, by a computerprocessor, a model build partition, including enabling a user to input,via a user interface: a storage location, file, folder, or library wherea modeling output is to be stored; a set of variables to be binned; aset of identifications and/or user selections for (i) modeling data,and/or (ii) a data model modeling methods to be created and/orprogrammed by the computer processor; and/or a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, (iii) a set of modeling methods, and/or (iv) model ensembleprocesses; (2) generating, by the computer processor, the modelingoutput according to the model build partition; and/or (3) displaying, inthe user interface, a set of results associated with generating themodeling output, the set of results including: (a) a set of model levelresults, and/or (b) a set of variable level results. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

In one implementation, enabling the user to input the set of variablesto be binned may include enabling the user to input, for each of the setof variables, (i) a binning technique, (ii) a number of bins, and/or(iii) a binned value. Generating the model build partition may furtherinclude enabling the user to input, via the user interface, (i) astratification selection, (ii) a sample percent, and/or (iii) a randomseed.

Enabling the user to input the set of identifications for the modelingdata may include enabling the user to input (i) a model type, (ii) adistribution, (iii) a link function, and/or (iv) a unique identifier.Additionally or alternatively, enabling the user to input the set ofidentifications for the modeling data may include enabling the user toinput a target risk variable and/or a target exposure variable.

Enabling the user to input the set of selections associated with theexploratory data analysis (EDA) may include enabling the user to inputwhether to run the EDA using the entire dataset or using the trainingdataset. Enabling the user to input the set of selections associatedwith the variable selection may include enabling the user to input (i)whether to run the variable selection using the entire dataset or usingthe training dataset, (ii) a set of model effects, and/or (iii) a set ofvariable selection techniques. Enabling the user to input the set ofselections associated with the modeling methods may include enabling theuser to input (i) whether to generate the modeling output using theentire dataset or using the training dataset, (ii) a model iterationidentification, and/or (iii) a set of model effects.

Displaying the set of model level results may include displaying, in theuser interface, a set of prediction statistics. Displaying the set ofvariable level results may include displaying, in the user interface, aset of main effects and/or a set of interaction relativity plots.

The modeling output may include multiple model outputs, and wherein themethod may further include combining multiple model outputs using eitheran additive technique or a multiplicative technique. In animplementation, the method may further include, after combining thefirst model output and the second model output: selecting a championmodel.

In another aspect, a computer system for enabling the management of datamodel and/or creating a data model may be provided. The system mayinclude a user interface; a memory storing a set of computer-executableinstructions; and/or a processor interfaced with the user interface andthe memory, and configured to execute the computer-executableinstructions to cause the processor to: (1) generate a model buildpartition, including enabling a user to input, via the user interface: astorage location, file, folder, or library where a modeling output is tobe stored; a set of variables to be binned; a set of identificationsand/or user selections for (i) modeling data, and/or (ii) modelingmethods to be created and/or programmed by the computer processor;and/or a set of selections associated with (i) an exploratory dataanalysis (EDA), (ii) a variable selection, (iii) a set of modelingmethod(s); and/or (iv) model ensemble process(es); (2) generate themodeling output according to the model build partition, and/or cause theuser interface to display a set of results associated with generatingthe modeling output, the set of results including: (a) a set model levelresults, and/or (b) a set of variable level results. The system mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In one implementation, the set of variables to be binned may include,for each of the set of variables, (i) a binning technique, (ii) a numberof bins, and/or (iii) a binned value.

In one implementation, wherein the set of identifications for themodeling data may include a target risk variable and/or a targetexposure variable. The set of selections associated with the exploratorydata analysis (EDA) may include whether to run the EDA using the entiredataset and/or using the training dataset.

The set of selections associated with the variable selection may include(i) whether to run the variable selection using the entire dataset orusing the training dataset, (ii) a set of model effects, and/or (iii) aset of variable selection techniques. The set of selections associatedwith the data model may include (i) whether to generate the modelingoutput using the entire dataset or using the training dataset, (ii) amodel iteration identification, and/or (iii) a set of model effects. Theset of model level results may include a set of prediction statistics.The set of variable level results may include a set of main effectsand/or a set of interaction relativity plots.

The modeling output may include a first model output and a second modeloutput, and wherein the processor is further configured to: combinemultiple outputs using either an additive technique or a multiplicativetechnique. The processor may be further configured to, after combiningmultiple model output: select a champion model.

In another aspect, a computer-implemented method of building a datamodel and then model ratemaking information may be provided. The methodmay include, via one or more processors: (1) accepting user input thatidentifies a file or folder from which to retrieve ratemaking inputdata; (2) accepting user input that identifies a file or folder to whichstore results generated from a user-defined data model created usinguser-selections acting upon the ratemaking input data; (3) acceptinguser selected variables or other selections (such as user selected iconsor buttons) related to the user-defined data model to be created; (4)translating the user selected variables or other selections into aprogramming language code that can be compiled, executed, and/or run byone or more processors to create the user-defined data model that isdefined by the user selected variables or other selections (or otherwisecreating the programming language code from the user selected variablesor other selections to alleviate the need for the user to have codingknowledge); (5) compiling, executing, and/or running the programminglanguage code to create the user-defined data model; (6) feeding theratemaking input data into the user-defined data model created to modelthe ratemaking input data and generate modeling results, or otherwisemodeling the ratemaking input data and generating the modeling results;and/or (7) displaying the modeling results, or other results generatedby the user-defined data model acting upon the ratemaking input data tofacilitate modeling ratemaking information. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

For instance, the user selected variables may include claim frequency,claim severity, pure premium, or loss ratio, and the data model may berelated to insurance.

The user selected variables may include vehicle, make, model, vehicleage, driver age, or marital status, and the user-defined data model maybe related to auto insurance. The user selected variables may alsorelate to autonomous or semi-autonomous vehicle features, systems, ortechnologies.

The user selected variables may include construction type, building age,or amount of insurance, and the user-defined data model may be relatedto homeowners insurance. The user selected variables may also relatesmart or interconnected home features, systems, or technologies.

The ratemaking input data and/or the user-defined data model may berelated to auto, homeowners, pet, renters, life, health, commercial,personal articles, or other types of insurance. Other embodiments arealso envisioned in which the input data and/or user-defined data modelare not related to insurance. For instance, the input data and/oruser-defined data model may be related to banking or financial services.

In another aspect, a computer system configured to build a data modeland then model ratemaking information may be provided. The computersystem may include one or more processors and/or memory units, and/or agraphical user interface, configured to: (1) accept user input thatidentifies a file or folder from which to retrieve ratemaking inputdata; (2) accept user input that identifies a file or folder to whichstore results generated from a user-defined data model created usinguser-selections acting upon the ratemaking input data; (3) accept userselected variables or other selections (such as user selected icons orbuttons) related to the user-defined data model to be created; (4)translate the user selected variables or other selections into aprogramming language code that can be compiled, executed, and/or run bythe one or more processors to create the user-defined data model that isdefined by the user selected variables or other selections (or otherwisecreate the programming language code from the user selected variables orother selections to alleviate the need for the user to have codingknowledge); (5) execute or run the programming language code to createthe user-defined data model; (6) feed the ratemaking input data into theuser-defined data model created to model the ratemaking input data andgenerate modeling results, or otherwise model the ratemaking input dataand generate modeling results; and/or (7) display the modeling results,or other results generated by the user-defined data model acting uponthe ratemaking input data to facilitate modeling ratemaking information.The system may include additional, less, or alternate functionalityand/or components, including those discussed elsewhere herein.

In one implementation, the user selected variables may include claimfrequency, claim severity, pure premium, or loss ratio, and the datamodel may be related to insurance. The user selected variables mayinclude type of vehicle, make, model, vehicle age, driver age, ormarital status, and the user-defined data model may be related to autoinsurance. Additionally or alternatively, the user selected variablesmay include construction type, building age, or amount of insurance, andthe user-defined data model may be related to homeowners insurance.

The ratemaking input data and/or the user-defined data model may berelated to auto, homeowners, pet, renters, life, health, commercial,personal articles, or other types of insurance.

In another aspect, a computer-implemented method of building a datamodel and then model input data may be provided. The method may include,via one or more processors: (1) accepting user input that identifies afile or folder from which to retrieve input data; (2) accepting userinput that identifies a file or folder to which store results generatedfrom a user-defined data model created using user-selections acting uponthe input data; (3) accepting user selected variables or otherselections (such as user selected icons or buttons) related to theuser-defined data model to be created; (4) translating, or otherwiseconverting, the user selected variables or other selections into aprogramming language code (such as the object code shown in some of theFigures herein) that can be compiled, executed, and/or run by one ormore processors to create the user-defined data model that is defined bythe user selected variables or other selections (or otherwise creatingthe programming language code from the user selected variables or otherselections to alleviate the need for the user to have coding knowledge);(5) compiling, executing, and/or running the programming language codeto create the user-defined data model; (6) feeding the input data intothe user-defined data model created to model the input data and generatemodeling results, or otherwise modeling the input data and generatingthe modeling results; and/or (7) displaying the modeling results, orother results generated by the user-defined data model acting upon theinput data to facilitate modeling input data. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In one implementation, the input data may be ratemaking input data, andthe user selected variables may include claim frequency, claim severity,pure premium, or loss ratio, and the data model may be related toinsurance. The input data may be ratemaking input data, and the userselected variables may include type of vehicle, make, model, vehicleage, driver age, or marital status, and the user-defined data model maybe related to auto insurance.

The input data may be ratemaking input data, and the user selectedvariables may include autonomous or semi-autonomous vehicle feature,system, or technology, and the user-defined data model may be related toauto insurance. The input data may be ratemaking input data, and theuser selected variables may include construction type, building age, oramount of insurance, and the user-defined data model may be related tohomeowners insurance.

The input data may be ratemaking input data, and the user selectedvariables may include smart or intelligent home system, technology, orfeature, and the user-defined data model may be related to homeownersinsurance. The input data may be ratemaking input data, and theratemaking input data and/or the user-defined data model may be relatedto auto, homeowners, pet, renters, life, health, commercial, personalarticles, or other types of insurance.

In another aspect, a computer system configured to build a data modeland then model input data may be provided. The computer system mayinclude one or more processors, and/or a graphical user interface,configured to: (1) accept user input that identifies a file or folderfrom which to retrieve input data; (2) accept user input that identifiesa file or folder to which store results generated from a user-defineddata model created using user-selections acting upon the input data; (3)accept user selected variables or other selections (such as userselected icons or buttons) related to the user-defined data model to becreated; (4) translate the user selected variables or other selectionsinto a programming language code (such as object or source code) thatcan be compiled, executed, and/or run by the one or more processors tocreate the user-defined data model that is defined by the user selectedvariables or other selections (or otherwise create the programminglanguage code from the user selected variables or other selections toalleviate the need for the user to have coding knowledge); (5) compile,execute, and/or run the programming language code to create theuser-defined data model; (6) feed the input data into the user-defineddata model created to model the input data and generate modelingresults, or otherwise model the input data and generate modelingresults; and/or (7) display the modeling results, or other resultsgenerated by the user-defined data model acting upon the input data tofacilitate modeling input data. The system may include additional, less,or alternate functionality and/or componentry, including that discussedelsewhere herein.

In one implementation, the input data may be ratemaking input data, andthe user selected variables may include claim frequency, claim severity,pure premium, or loss ratio, and the data model may be related toinsurance, and/or the user selected variables may include type ofvehicle, make, model, vehicle age, driver age, or marital status, andthe user-defined data model may be related to auto insurance.

The input data may be ratemaking input data, and the user selectedvariables may include autonomous or semi-autonomous vehicle feature,system, or technology, and the user-defined data model may be related toauto insurance, and/or may include construction type, building age, oramount of insurance, and the user-defined data model may be related tohomeowners insurance.

The input data may be ratemaking input data, and the user selectedvariables may include smart or intelligent home system, technology, orfeature, and the user-defined data model may be related to homeownersinsurance. Additionally or alternatively, the ratemaking input dataand/or the user-defined data model may be related to auto, homeowners,pet, renters, life, health, commercial, personal articles, or othertypes of insurance.

In another aspect, a graphical user interface configured to facilitatebuilding a data model and then model input data may be provided. Thegraphical user interface configured to: (1) accept user input thatidentifies a file or folder from which to retrieve input data; (2)accept user input that identifies a file or folder to which storeresults generated from a user-defined data model created usinguser-selections acting upon the input data; (3) accept user selectedvariables or other selections (such as user selected icons or buttons)related to the user-defined data model to be created; (4) displayprogramming language code that can be compiled, executed, and/or run byone or more processors to create the user-defined data model that isdefined by the user selected variables or other selections, theprogramming language code being generated or processor created by one ormore processors using at least in part the user selected variables orother selections to alleviate the need for the user to have codingknowledge; and/or (5) display modeling results generated by the inputdata that is retrieved being feed into or otherwise analyzed by theuser-defined data model created using the programming language code thatis generated or otherwise processor created based at least in part uponthe user selected variables or other selections, and/or other resultsgenerated by the user-defined data model acting upon the input data tofacilitate modeling input data. The user interface may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

In one implementation, the input data may be ratemaking input data, andthe user selected variables may include claim frequency, claim severity,pure premium, or loss ratio, and the data model may be related toinsurance; type of vehicle, make, model, vehicle age, driver age, ormarital status; and/or autonomous or semi-autonomous vehicle feature,system, or technology, and the user-defined data model may be related toauto insurance.

The input data may be ratemaking input data, and the user selectedvariables may include construction type, building age, or amount ofinsurance, and the user-defined data model may be related to homeownersinsurance. The input data may be ratemaking input data, and the userselected variables may include smart or intelligent home system,technology, or feature, and the user-defined data model may be relatedto homeowners insurance. Additionally or alternatively, the input datamay be ratemaking input data, and the ratemaking input data and/or theuser-defined data model may be related to auto, homeowners, pet,renters, life, health, commercial, personal articles, or other types ofinsurance.

Additional Exemplary GLM Embodiments

In another aspect, a computer-implemented method in a computing deviceof enabling the management of data models may be provided. The methodmay include (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or a Generalized Linear Model (GLM) to be createdand/or programmed by the computer processor; and/or a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, and/or (iii) a challenger model comparison; (2) generating,by the computer processor, the modeling output according to the modelbuild partition; and/or (3) displaying, in the user interface, a set ofresults associated with generating the modeling output, the set ofresults including: (a) a set of model level results, and/or (b) a set ofvariable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The system may include: a user interface; amemory storing a set of computer-executable instructions; and/or aprocessor interfaced with the user interface and the memory, andconfigured to execute the computer-executable instructions to cause theprocessor to: (1) generate a model build partition, including enabling auser to input, via the user interface: a storage location where amodeling output is to be stored; a set of variables to be binned; a setof identifications and/or user selections for modeling data, and/or aGeneralized Linear Model (GLM) to be created and/or programmed by thecomputer processor, and/or a set of selections associated with (i) anexploratory data analysis (EDA), (ii) a variable selection, and/or (iii)a challenger model comparison; (2) generate the modeling outputaccording to the model build partition, and/or (3) cause the userinterface to display a set of results associated with generating themodeling output, the set of results including: (a) a set model levelresults, and/or (b) a set of variable level results. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method for building aGeneralized Linear Model (GLM) model and then model ratemakinginformation may be provided. The method may include, via one or moreprocessors: (1) accepting user input that identifies a file from whichto retrieve ratemaking input data; (2) accepting user input thatidentifies a file to which store results generated from a user-definedGLM created using user-selections acting upon the ratemaking input data;(3) accepting user-selected variables related to the user-defined GLM tobe created; (4) translating the user-selected variables into aprogramming language code that can be compiled, executed, and/or run byone or more processors to create the user-defined GLM that is defined bythe user-selected variables; (5) executing the programming language codeto create the user-defined GLM; (6) feeding the ratemaking input datainto the user-defined GLM created to model the ratemaking input data andgenerate modeling results; and/or (7) displaying the modeling results tofacilitate modeling ratemaking information. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In another aspect, a computer system configured to build a GeneralizedLinear Model (GLM) model and then model ratemaking information may beprovided. The computer system may include one or more processors, and/ora graphical user interface, configured to: (1) accept user input thatidentifies a file from which to retrieve ratemaking input data; (2)accept user input that identifies a file to which store resultsgenerated from a user-defined GLM created using user-selections actingupon the ratemaking input data; (3) accept user-selected variablesrelated to the user-defined GLM to be created; (4) translate theuser-selected variables into a programming language code that can becompiled, executed, and/or run by the one or more processors to createthe user-defined GLM that is defined by the user-selected variables; (5)execute the programming language code to create the user-defined GLM;(6) feed the ratemaking input data into the user-defined GLM created tomodel the ratemaking input data and generate modeling results; and/or(7) display the modeling results to facilitate modeling ratemakinginformation. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method of building aGeneralized Linear Model (GLM) model and then model input data may beprovided. The method may include, via one or more processors: (1)accepting user input that identifies a file from which to retrieve inputdata; (2) accepting user input that identifies a file to which storeresults generated from a user-defined GLM created using user-selectionsacting upon the input data; (3) accepting user-selected variablesrelated to the user-defined GLM to be created; (4) translating theuser-selected variables into a programming language code that can becompiled, executed, and/or run by one or more processors to create theuser-defined GLM that is defined by the user-selected variables; (5)executing the programming language code to create the user-defined GLM;(6) feeding the input data into the user-defined GLM created to modelthe input data and generate modeling results; and/or (7) displaying themodeling results to facilitate modeling input data. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Additional Exemplary GAM Embodiments

In one aspect, a computer-implemented method in a computing device ofenabling the management of data models may be provided. The method mayinclude (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or a Generalized Additive Model (GAM) to becreated and/or programmed by the computer processor; and/or a set ofselections associated with (i) an exploratory data analysis (EDA), (ii)a variable selection, and/or (iii) a challenger model comparison; (2)generating, by the computer processor, the modeling output according tothe model build partition; and/or (3) displaying, in the user interface,a set of results associated with generating the modeling output, the setof results including: (a) a set of model level results, and/or (b) a setof variable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The method may include: a user interface; amemory storing a set of computer-executable instructions; and aprocessor interfaced with the user interface and the memory, andconfigured to execute the computer-executable instructions to cause theprocessor to: (1) generate a model build partition, including enabling auser to input, via the user interface: a storage location where amodeling output is to be stored; a set of variables to be binned; a setof identifications and/or user selections for modeling data, and/or aGeneralized Additive Model (GAM) to be created and/or programmed by thecomputer processor; and/or a set of selections associated with (i) anexploratory data analysis (EDA), (ii) a variable selection, and/or (iii)a challenger model comparison; (2) generate the modeling outputaccording to the model build partition, and/or (3) cause the userinterface to display a set of results associated with generating themodeling output, the set of results including: (a) a set model levelresults, and/or (b) a set of variable level results. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-implemented method for building aGeneralized Additive Model (GAM) model and then model ratemakinginformation may be provided. The method may include, via one or moreprocessors: (1) accepting user input that identifies a file from whichto retrieve ratemaking input data; (2) accepting user input thatidentifies a file to which store results generated from a user-definedGAM created using user-selections acting upon the ratemaking input data;(3) accepting user-selected variables related to the user-defined GAM tobe created; (4) translating the user-selected variables into aprogramming language code that can be compiled, executed, and/or run byone or more processors to create the user-defined GAM that is defined bythe user-selected variables; (5) executing the programming language codeto create the user-defined GAM; (6) feeding the ratemaking input datainto the user-defined GAM created to model the ratemaking input data andgenerate modeling results; and/or (7) displaying the modeling results tofacilitate modeling ratemaking information. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In another aspect, a computer system configured to build a GeneralizedAdditive Model (GAM) model and then model ratemaking information may beprovided. The computer system may include one or more processors, and/ora graphical user interface, configured to: (1) accept user input thatidentifies a file from which to retrieve ratemaking input data; (2)accept user input that identifies a file to which store resultsgenerated from a user-defined GAM created using user-selections actingupon the ratemaking input data; (3) accept user-selected variablesrelated to the user-defined GAM to be created; (4) translate theuser-selected variables into a programming language code that can becompiled, executed, and/or run by the one or more processors to createthe user-defined GAM that is defined by the user-selected variables; (5)execute the programming language code to create the user-defined GAM;(6) feed the ratemaking input data into the user-defined GAM created tomodel the ratemaking input data and generate modeling results; and/or(7) display the modeling results to facilitate modeling ratemakinginformation. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method for building aGeneralized Additive Model (GAM) model and then model input data may beprovided. The method may include, via one or more processors: (1)accepting user input that identifies a file from which to retrieve inputdata; (2) accepting user input that identifies a file to which storeresults generated from a user-defined GAM created using user-selectionsacting upon the input data; (3) accepting user-selected variablesrelated to the user-defined GAM to be created; (4) translating theuser-selected variables into a programming language code that can becompiled, executed, and/or run by one or more processors to create theuser-defined GAM that is defined by the user-selected variables; (4)executing the programming language code to create the user-defined GAM;(5) feeding the input data into the user-defined GAM created to modelthe input data and generate modeling results; and/or (6) displaying themodeling results to facilitate modeling input data. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Additional Exemplary ELM Embodiments

In one aspect, a computer-implemented method in a computing device ofenabling the management of data models may be provided. The method mayinclude (1) generating, by a computer processor, a model buildpartition, including enabling a user to input, via a user interface: astorage location where a modeling output is to be stored; a set ofvariables to be binned; a set of identifications and/or user selectionsfor modeling data, and/or an Ensemble Learning Method (ELM) to becreated and/or programmed by the computer processor; and/or a set ofselections associated with (i) an exploratory data analysis (EDA), (ii)a variable selection, and/or (iii) a challenger model comparison; (2)generating, by the computer processor, the modeling output according tothe model build partition; and/or (3) displaying, in the user interface,a set of results associated with generating the modeling output, the setof results including: (a) a set of model level results, and/or (b) a setof variable level results. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer system for enabling the management of datamodels may be provided. The computer system may include: a userinterface; a memory storing a set of computer-executable instructions;and/or a processor interfaced with the user interface and the memory,and configured to execute the computer-executable instructions to causethe processor to: (1) generate a model build partition, includingenabling a user to input, via the user interface: a storage locationwhere a modeling output is to be stored; a set of variables to bebinned; a set of identifications and/or user selections for modelingdata, and/or an Ensemble Learning Method (ELM) to be created and/orprogrammed by the computer processor; and/or a set of selectionsassociated with (i) an exploratory data analysis (EDA), (ii) a variableselection, and/or (iii) a challenger model comparison; (2) generate themodeling output according to the model build partition, and/or (3) causethe user interface to display a set of results associated withgenerating the modeling output, the set of results including: (a) a setmodel level results, and/or (b) a set of variable level results. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method for building anEnsemble Learning Method (ELM) model and then model ratemakinginformation may be provided. The method may include, via one or moreprocessors: (1) accepting user input that identifies a file from whichto retrieve ratemaking input data; (2) accepting user input thatidentifies a file to which store results generated from a user-definedELM created using user-selections acting upon the ratemaking input data;(3) accepting user-selected variables related to the user-defined ELM tobe created; (4) translating the user-selected variables into aprogramming language code that can be compiled, executed, and/or run byone or more processors to create the user-defined ELM that is defined bythe user-selected variables; (5) executing the programming language codeto create the user-defined ELM; (6) feeding the ratemaking input datainto the user-defined ELM created to model the ratemaking input data andgenerate modeling results; and/or (7) displaying the modeling results tofacilitate modeling ratemaking information. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In another aspect, a computer system configured to build an EnsembleLearning Method (ELM) model and then model ratemaking information may beprovided. The computer system may include one or more processors, and/ora graphical user interface, configured to (1) accept user input thatidentifies a file from which to retrieve ratemaking input data; (2)accept user input that identifies a file to which store resultsgenerated from a user-defined ELM created using user-selections actingupon the ratemaking input data; (3) accept user-selected variablesrelated to the user-defined ELM to be created; (4) translate theuser-selected variables into a programming language code that can becompiled, executed, and/or run by the one or more processors to createthe user-defined ELM that is defined by the user-selected variables; (5)execute the programming language code to create the user-defined ELM;(6) feed the ratemaking input data into the user-defined ELM created tomodel the ratemaking input data and generate modeling results; and/or(7) display the modeling results to facilitate modeling ratemakinginformation. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method of building an EnsembleLearning Method (ELM) model and then model input data may be provided.The method may include, via one or more processors: (1) accepting userinput that identifies a file from which to retrieve input data; (2)accepting user input that identifies a file to which store resultsgenerated from a user-defined ELM created using user-selections actingupon the input data; (3) accepting user-selected variables related tothe user-defined ELM to be created; (4) translating the user-selectedvariables into a programming language code that can be compiled,executed, and/or run by one or more processors to create theuser-defined ELM that is defined by the user-selected variables; (5)executing the programming language code to create the user-defined ELM;(6) feeding the input data into the user-defined ELM created to modelthe input data and generate modeling results; and/or (7) displaying themodeling results to facilitate modeling input data. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Additional Considerations

Although the text herein sets forth a detailed description of numerousdifferent embodiments, it should be understood that the legal scope ofthe invention is defined by the words of the claims set forth at the endof this patent. The detailed description is to be construed as exemplaryonly and does not describe every possible embodiment, as describingevery possible embodiment would be impractical, if not impossible. Onecould implement numerous alternate embodiments, using either currenttechnology or technology developed after the filing date of this patent,which would still fall within the scope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘______’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based upon any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this disclosureis referred to in this disclosure in a manner consistent with a singlemeaning, that is done for sake of clarity only so as to not confuse thereader, and it is not intended that such claim term be limited, byimplication or otherwise, to that single meaning.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

At various points herein, methods have been described as involving afirst, second, and/or third block of a blockchain. It should beappreciated that the labels first, second, and third are used for easeof explanation and does not necessarily imply the involvement ofmultiple blocks. To this end, all transactions described as beingincluded in a first, second, and/or third block may, in implementations,be included in just a single block of the blockchain.

Additionally, although the systems and methods described herein describefunctionality at particular nodes of the blockchain, such descriptionsare done for ease of explanation. To this end, any functionallydescribed as occurring at two separate nodes may be implemented at asingle node. Similarly, any functionality described as occurring at asingle node, may be implemented across any number of nodes.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (code embodied on anon-transitory, tangible machine-readable medium) or hardware. Inhardware, the routines, etc., are tangible units capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa module that operates to perform certain operations as describedherein.

In various embodiments, a module may be implemented mechanically orelectronically. Accordingly, the term “module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich modules are temporarily configured (e.g., programmed), each of themodules need not be configured or instantiated at any one instance intime. For example, where the modules comprise a general-purposeprocessor configured using software, the general-purpose processor maybe configured as respective different modules at different times.Software may accordingly configure a processor, for example, toconstitute a particular module at one instance of time and to constitutea different module at a different instance of time.

Modules can provide information to, and receive information from, othermodules. Accordingly, the described modules may be regarded as beingcommunicatively coupled. Where multiple of such modules existcontemporaneously, communications may be achieved through signaltransmission (e.g., over appropriate circuits and buses) that connectthe modules. In some embodiments in which multiple modules areconfigured or instantiated at different times, communications betweensuch modules may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplemodules have access. For example, one module may perform an operationand store the output of that operation in a memory device to which it iscommunicatively coupled. A further module may then, at a later time,access the memory device to retrieve and process the stored output.Modules may also initiate communications with input or output devices,and can operate on a resource (e.g., a collection of information).

The various operations of exemplary methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some exemplary embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation. Some embodiments may be described using the expression“coupled” and “connected” along with their derivatives. For example,some embodiments may be described using the term “coupled” to indicatethat two or more elements are in direct physical or electrical contact.The term “coupled,” however, may also mean that two or more elements arenot in direct contact with each other, but yet still co-operate orinteract with each other. The embodiments are not limited in thiscontext.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment. In addition, use of the “a” or “an” are employed todescribe elements and components of the embodiments herein. This is donemerely for convenience and to give a general sense of the description.This description, and the claims that follow, should be read to includeone or at least one and the singular also includes the plural unless itis obvious that it is meant otherwise.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application. Uponreading this disclosure, those of skill in the art will appreciate stilladditional alternative structural and functional designs for system anda method for assigning mobile device data to a vehicle through thedisclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

The particular features, structures, or characteristics of any specificembodiment may be combined in any suitable manner and in any suitablecombination with one or more other embodiments, including the use ofselected features without corresponding use of other features. Inaddition, many modifications may be made to adapt a particularapplication, situation or material to the essential scope and spirit ofthe present invention. It is to be understood that other variations andmodifications of the embodiments of the present invention described andillustrated herein are possible in light of the teachings herein and areto be considered part of the spirit and scope of the present invention.

While the preferred embodiments of the invention have been described, itshould be understood that the invention is not so limited andmodifications may be made without departing from the invention. Thescope of the invention is defined by the appended claims, and alldevices that come within the meaning of the claims, either literally orby equivalence, are intended to be embraced therein. It is thereforeintended that the foregoing detailed description be regarded asillustrative rather than limiting, and that it be understood that it isthe following claims, including all equivalents, that are intended todefine the spirit and scope of this invention.

What is claimed:
 1. A computer-implemented method in a computing deviceof enabling the management of data models, the method comprising:generating, by a computer processor, a model build partition, includingenabling a user to input, via a user interface: a storage location wherea modeling output is to be stored, a set of variables to be binned, aset of identifications for (1) at least one of a training dataset and avalidation dataset, and (2) modeling data, and a set of selectionsassociated with at least one of: (i) an exploratory data analysis (EDA),(ii) a variable selection, and (iii) a challenger model comparison;generating, by the processor, the modeling output according to the modelbuild partition; and displaying, in the user interface, a set of resultsassociated with generating the modeling output, the set of resultsincluding: (a) a set of model level results, and (b) a set of variablelevel results.
 2. The computer-implemented method of claim 1, whereinenabling the user to input the set of variables to be binned comprises:enabling the user to input, for each of the set of variables, (i) abinning technique, (ii) a number of bins, and (iii) a binned value. 3.The computer-implemented method of claim 1, wherein generating the modelbuild partition further includes: enabling the user to input, via theuser interface, (i) a stratification selection, (ii) a sample percent,and (iii) a random seed.
 4. The computer-implemented method of claim 1,wherein enabling the user to input the set of identifications for themodeling data comprises: enabling the user to input (i) a model type,(ii) a distribution, (iii) a link function, and (iv) a uniqueidentifier.
 5. The computer-implemented method of claim 1, whereinenabling the user to input the set of identifications for the modelingdata comprises: enabling the user to input a target risk variable and atarget exposure variable.
 6. The computer-implemented method of claim 1,wherein enabling the user to input the set of selections associated withthe exploratory data analysis (EDA) comprises: enabling the user toinput whether to run the EDA using the training dataset and thevalidation dataset, or using the training dataset.
 7. Thecomputer-implemented method of claim 1, wherein enabling the user toinput the set of selections associated with the variable selectioncomprises: enabling the user to input (i) whether to run the variableselection using the training dataset and the validation dataset, orusing the training dataset, (ii) a set of model effects, and (iii) a setof variable selection techniques.
 8. The computer-implemented method ofclaim 1, wherein displaying the set of model level results comprises:displaying, in the user interface, a set of prediction statistics. 9.The computer-implemented method of claim 1, wherein displaying the setof variable level results comprises: displaying, in the user interface,a set of main effects and a set of interaction relativity plots.
 10. Thecomputer-implemented method of claim 1, wherein the modeling outputcomprises a first model output and a second model output, and whereinthe method further comprises: combining the first model output and thesecond model output using either an additive technique or amultiplicative technique.
 11. A system for enabling the management ofdata models, comprising: a user interface; a memory storing a set ofcomputer-executable instructions; and a processor interfaced with theuser interface and the memory, and configured to execute thecomputer-executable instructions to cause the processor to: generate amodel build partition, including enabling a user to input, via the userinterface: a storage location where a modeling output is to be stored, aset of variables to be binned, a set of identifications for (1) at leastone of a training dataset and a validation dataset, and (2) modelingdata, and a set of selections associated with at least one of: (i) anexploratory data analysis (EDA), (ii) a variable selection, and (iii) achallenger model comparison, generate the modeling output according tothe model build partition, and cause the user interface to display a setof results associated with generating the modeling output, the set ofresults including: (a) a set model level results, and (b) a set ofvariable level results.
 12. The system of claim 11, wherein the set ofvariables to be binned comprises, for each of the set of variables, (i)a binning technique, (ii) a number of bins, and (iii) a binned value.13. The system of claim 11, to generating the model build partition, theprocessor is further configured to: enable the user to input, via theuser interface, (i) a stratification selection, (ii) a sample percent,and (iii) a random seed.
 14. The system of claim 11, wherein the set ofidentifications for the modeling data comprises (i) a model type, (ii) adistribution, (iii) a link function, and (iv) a unique identifier. 15.The system of claim 11, wherein the set of identifications for themodeling data comprises a target risk variable and a target exposurevariable.
 16. The system of claim 11, wherein the set of selectionsassociated with the exploratory data analysis (EDA) comprises whether torun the EDA using the training dataset and the validation dataset, orusing the training dataset.
 17. The system of claim 11, wherein the setof selections associated with the variable selection comprises (i)whether to run the variable selection using the training dataset and thevalidation dataset, or using the training dataset, (ii) a set of modeleffects, and (iii) a set of variable selection techniques.
 18. Thesystem of claim 11, wherein the set of model level results comprises aset of prediction statistics.
 19. The system of claim 11, wherein theset of variable level results comprises a set of main effects and a setof interaction relativity plots.
 20. The system of claim 11, wherein themodeling output comprises a first model output and a second modeloutput, and wherein the processor is further configured to: combine thefirst model output and the second model output using either an additivetechnique or a multiplicative technique.